A practical predictive model to predict 30-day mortality in neonatal sepsis

SUMMARY OBJECTIVE: Neonatal sepsis is a serious disease that needs timely and immediate medical attention. So far, there is no specific prognostic biomarkers or model for dependable predict outcomes in neonatal sepsis. The aim of this study was to establish a predictive model based on readily available laboratory data to assess 30-day mortality in neonatal sepsis. METHODS: Neonates with sepsis were recruited between January 2019 and December 2022. The admission information was obtained from the medical record retrospectively. Univariate or multivariate analysis was utilized to identify independent risk factors. The receiver operating characteristic curve was drawn to check the performance of the predictive model. RESULTS: A total of 195 patients were recruited. There was a big difference between the two groups in the levels of hemoglobin and prothrombin time. Multivariate analysis confirmed that hemoglobin>133 g/L (hazard ratio: 0.351, p=0.042) and prothrombin time >16.6 s (hazard ratio: 4.140, p=0.005) were independent risk markers of 30-day mortality. Based on these results, a predictive model with the highest area under the curve (0.756) was built. CONCLUSION: We established a predictive model that can objectively and accurately predict individualized risk of 30-day mortality. The predictive model should help clinicians to improve individual treatment, make clinical decisions, and guide follow-up management strategies.


INTRODUCTION
Neonatal sepsis is a serious and life-threatening disease that needs timely and immediate medical attention 1 .Despite advances in medical care, neonatal sepsis remains a significant cause of morbidity and mortality in neonates worldwide.The prevalence of neonatal sepsis and mortality rates varies across different regions and healthcare settings, with higher rates reported in low-resource areas 2 .Studies have reported that various biomarkers can affect the prognosis of neonatal sepsis, including gestational age, birth weight, presence of comorbidities, blood change, and the type of infecting organism [3][4][5][6][7] .Early identification and suitable treatment are essential for improving outcomes in patients with sepsis.To neonates, the clinical manifestations of sepsis can be nonspecific.Laboratory investigations, such as blood pressure monitoring, interleukin-18, and elevated neutrophil-to-monocyte ratio, are helpful for clinicians to evaluate the risk of adverse outcomes and guide treatment decisions [8][9][10] .So far, there is no specific prognostic biomarkers or model for dependable predict outcomes in neonatal sepsis.Our goal is to establish a predictive model based on readily available laboratory data to assess 30-day mortality in neonatal sepsis.

Patients
The investigation recruited neonates who were admitted to the hospital between January 2019 and December 2022.The admission information was obtained from the medical record retrospectively.The neonates were limited to those who were diagnosed with neonatal sepsis, with complete patient information, and aged within 28 days.Finally, 195 patients were recruited in the cohort.The diagnosis of neonatal sepsis was based on the International Consensus on Pediatric Sepsis definition 11 .The study was approved by the ethics committee at the local hospital and was conducted in accordance with the guidelines set out in the Declaration of Helsinki.

Statistical analysis
All statistical analyses were conducted with SPSS 21.0 (SPSS, Inc., IA, USA).Continuous variables, shown as mean±standard deviation, were compared with t-test and analysis of variance (ANOVA).Categorical variables (numbers and percentages) were compared using the chi-square test.Univariate or multivariate analysis was utilized to identify independent risk factors for 30-day mortality.Patients were divided into two groups according to the primary outcome.The receiver operating characteristic (ROC) curve was drawn to check the performance of the model in predicting the primary events and confirmed the optimal cutoff value of the predictive model.All tests were two-sided, and p-values <0.05 were considered statistically significant.

Baseline characteristics
The basic information of patients is presented in Table 1.Of these patients, the 30-day mortality rate was 17.5%.There were 123 males and 72 females, with a median age of 3 days ( ranging from 1 day to 28 days).Compared with the nonsurvivor group, the significant elevated levels of platelet and ALB and the significant declined levels of ALT, AST, UREA, PT, INR, and TT were found in the survivor group (all p<0.05).Beyond that, obvious difference was also found in the numbers of culture positive, the levels of hemoglobin, LDH, DB, and CREA between the two groups, although statistical significance was not reached (all p<0.10) (Table 1).

Performance of the predictive model in the prediction of 30-day mortality
Sensitivity and specificity were determined to compare the performance of the predictive model and independent predictors.Figure 1A shows the ROC curve of the predictive model; the predictive model had the highest area under the curve (AUC) (0.756, 95%CI 0.666-0.847,P<0.001).The calibration curve of the predictive model had demonstrated good agreement (Figure 1B).Details of the performance are shown in Supplementary Table 1.
In addition, the patients were divided into two groups based on the predictive model.A comparison was made between the two groups; the results showed that the 30-day mortality rate was higher in the predictive model value>0.05588groups than that in the predictive model value ≤ 0.05588 groups (24.3 vs. 3.40%).

DISCUSSION
In this study, we found a significant association among HB, PT, and the risk of 30-day mortality.Based on independent risk factors, we built a predictive model with good performance.In addition, we found that the 30-day mortality rate was higher in the predictive model value>0.05588 group than that in the predictive model value ≤ 0.05588 group.
Neonatal sepsis refers to a severe bloodstream infection that occurs in neonates.It is often accompanied by a characterized systemic inflammatory response syndrome 12 .Inflammation plays a vital role in the pathogenesis and progression of neonatal sepsis 13 .During neonatal sepsis, the presence of pathogens triggers an immune response, leading to the release of various pro-inflammatory molecules such as cytokines, chemokines, and acute-phase reactants 14,15 .However, the overproduction of pro-inflammatory molecules can result in widespread tissue damage, organ dysfunction, and complications associated with sepsis 16 .
Hemoglobin is a major component of red blood cells, and its measurement provides information about anemia and oxygenation status 17 .In neonatal sepsis, the infection and inflammatory response can impact the hematopoietic system, Qiao T et al.
potentially leading to the development of anemia 18 .The release of inflammatory mediators and activation of inflammatory cells may suppress red blood cell production or promote their destruction, thereby reducing hemoglobin levels.Furthermore, sepsis can also cause systemic hemodynamic changes such as tissue hypoperfusion and circulatory disturbances, which can influence blood hemoglobin levels 19 .In line with prior studies, our study also confirmed that HB was an independent index.Therefore, monitoring hemoglobin levels in neonates with sepsis can provide valuable information about the severity of anemia, inflammatory response, and overall circulatory status.This aids clinicians in assessing the disease severity, guiding treatment strategies, and monitoring treatment effectiveness.
Prothrombin time, a test that measures the duration taken for the blood to clot, is an important indicator of coagulation function and can provide insights into the body's ability to form blood clots 20 .In neonatal sepsis, the inflammatory response and activation of coagulation pathways can lead to alterations in the coagulation system 21 .Sepsis-induced changes in the levels of coagulation factors, platelets, and endothelial cells can affect the clotting process and prolong PT 22,23 .Additionally, disseminated intravascular coagulation, a severe complication associated with sepsis, can further disrupt the coagulation cascade and contribute to abnormal PT results 24 .By monitoring PT in neonates with sepsis, clinicians can gain insights into the coagulation status and identify potential clotting abnormalities.This information is crucial for guiding appropriate treatment strategies, such as the administration of blood products or anticoagulants, and improving patient outcomes.Our study also confirmed this.
For clinical application, it is important to make the assessment of risk factors as convenient as possible.In our study, HB and PT are prevalent in clinical practice and convenient to acquire.

A predictive model in neonatal sepsis
They were independent factors demonstrated by multivariate analysis and we built a simple, convincing, and readily available model with good performance.On subgroup analysis, the 30-day mortality rate was higher in the predictive model value>0.05588groups than that in the predictive model value ≤ 0.05588 groups.
Identifying high-risk patients may help clinicians improve the treatment efficacy and clinical outcome.This study is limited by its retrospective nature and single-center data, which may cause selection bias.Second, only admission biomarkers were included in the present analyses, and it is  Qiao T et al.
possible that dynamic changes in biomarkers during the course of treatment might also influence outcomes in neonatal sepsis.Third, there were no test results for inflammatory factors, such as procalcitonin, and interleukin-6 (IL-6).Fourth, there are no external and internal cohorts.Thus, before clinical application, large, multicenter, prospective studies and validation cohort need to be conducted to determine the value of the predictive model.

CONCLUSION
Based on the clinical risk factors identified in this cohort, we established a model that can objectively and accurately predict individualized risk of 30-day mortality.The predictive model should help clinicians to improve individual treatment, make clinical decisions, and guide follow-up management strategies.

Figure 1 .
Figure 1.Performance of the predictive model.(A) Receiver operating characteristic curves of factors for predicting mortality.(B) Calibration curves.

Table 1 .
Characteristics of patients.

Table 2 .
Analysis of in-hospital death.